APP FILE DONE

app.py

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+from streamlit_extras.let_it_rain import rain
+import streamlit as st
+import pickle
+import string
+from nltk.corpus import stopwords
+import nltk
+from nltk.stem.porter import PorterStemmer
+import sklearn
+ps = PorterStemmer()
+
+def example():
+    rain(
+        emoji="❌",
+        font_size=64,
+        falling_speed=1.5,
+        animation_length="10",
+    )
+
+def transform_Text(Text):
+    Text = Text.lower()
+    Text = nltk.word_tokenize(Text)
+
+    y = []
+    for i in Text:
+        if i.isalnum():
+            y.append(i)
+
+    Text = y[:]
+    y.clear()
+
+    for i in Text:
+        if i not in stopwords.words('english') and i not in string.punctuation:
+            y.append(i)
+
+    Text = y[:]
+    y.clear()
+
+    for i in Text:
+        y.append(ps.stem(i))
+
+    return " ".join(y)
+
+
+tfidf = pickle.load(open('vectorizer.pkl', 'rb'))
+model = pickle.load(open('model.pkl', 'rb'))
+
+st.title("SMS SPAM CLASSIFIER/CHECKER")
+st.text("")
+st.text("")
+input_sms = st.text_area("Enter the message...or Copy and Paste the message to detect!! ",)
+st.text("")
+st.write(f'You wrote {len(input_sms)} characters.')
+st.text("")
+if st.button("Let's Check"):
+
+    transformed_sms = transform_Text(input_sms)
+    vector_input = tfidf.transform([transformed_sms])
+    result = model.predict(vector_input)[0]
+    if result == 1:
+     st.warning("OH..NO! IT'S A SPAM !! BEAWARE!")
+     example()
+    else:
+     st.success("RELAX!! IT'S NOT A SPAM !")
+     st.balloons()
+